
# Import Libraries
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split 
import seaborn as sns
from matplotlib import pyplot as plt

2021-11-20 21:44:59.136142: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-11-20 21:44:59.136249: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.


# Load the TensorBoard notebook extension.
%load_ext tensorboard
from datetime import datetime
from packaging import version

print("TensorFlow version: ", tf.__version__)
assert version.parse(tf.__version__).release[0] >= 2, \
    "This notebook requires TensorFlow 2.0 or above."
import tensorboard
tensorboard.__version__

TensorFlow version:  2.7.0

'2.7.0'


import random
random.seed(20211120)
tf.random.set_seed(20211120)
np.random.seed(20211120)


# loading the large data set, it may takes a while.
emission_train = pd.read_csv("emission.csv", delimiter=",", quoting = 3)


display(emission_train.head(100))
display(emission_train.describe())


correlation_graph_data = emission_train.sample(frac=0.05).reset_index(drop=True)
print(len(emission_train), 'emission_train')
print(len(correlation_graph_data), 'correlation_graph_data')
sns.pairplot(correlation_graph_data[['vehicle_CO2', 'vehicle_angle', 'vehicle_fuel', 'vehicle_noise', 'vehicle_pos', 'vehicle_speed', 'vehicle_waiting', 'vehicle_x', 'vehicle_y']], diag_kind='kde')

#Free up memory for Python
del correlation_graph_data

16331008 emission_train
816550 correlation_graph_data


emission_train = emission_train.drop(columns=["vehicle_CO", "vehicle_HC", "vehicle_NOx", "vehicle_PMx", 
                                              "timestep_time", "vehicle_id", "vehicle_lane", "vehicle_electricity",
                                              "vehicle_x", "vehicle_y"])


display(emission_train.head(100))
display(emission_train.describe())

### Insert your code below ###
display(emission_train.tail(100))


for header in ["vehicle_eclass", "vehicle_type"]:
    emission_train[header] = emission_train[header].astype(str)

emission_train = emission_train.dropna().reset_index(drop=True)

# Shuffle the dataset
emission_train_shuffle = emission_train.sample(frac=1)

### Insert your code below ###

# Display the data pre- and post- shuffle
display(emission_train.head(100))
###FILL IN THE CODE
display(emission_train_shuffle.head(100))

# Get info of the dataframe
###FILL IN THE CODE
display(emission_train.describe())

display(emission_train_shuffle.describe())


random.seed(20211120)
tf.random.set_seed(20211120)
np.random.seed(20211120)

train_df, backup_df = train_test_split(emission_train_shuffle, test_size=0.99) # Comment this line for large data training
# Edit the test_size below.

# train_df, test_df = train_test_split(emission_train_shuffle, test_size=0.1) # Uncomment for large dataset
train_df, test_df = train_test_split(train_df, test_size=0.1) # Comment for large dataset
train_df, val_df = train_test_split(train_df, test_size=0.1)

print(len(backup_df), 'backup data')
print(len(train_df), 'train examples')
print(len(val_df), 'validation examples')
print(len(test_df), 'test examples')

#del emission_train

16167697 backup data
132281 train examples
14698 validation examples
16331 test examples


# # Z-Score Normalizing
train_df_norm = train_df
val_df_norm = val_df
test_def_norm = test_df

for header in ["vehicle_noise", "vehicle_speed", "vehicle_waiting"]:
    mean = train_df[header].mean()
    std = train_df[header].std()
    train_df_norm[header] = (train_df[header] - mean) / std
    train_df_norm[header] = train_df_norm[header].fillna(0)


### Insert your code below (optional) ###
# Normalize the validation data
    val_df_norm[header] = (val_df[header] - mean) / std
    val_df_norm[header] = val_df_norm[header].fillna(0)


# Normalize the test data
    test_def_norm[header] = (test_def_norm[header] - mean) / std
    test_def_norm[header] = test_def_norm[header].fillna(0)


print(train_df_norm.head())

          vehicle_CO2  vehicle_angle   vehicle_eclass  vehicle_fuel  \
7638310       2549.70         203.05  HBEFA3/PC_G_EU4          1.10   
11969572      2624.72         181.45  HBEFA3/PC_G_EU4          1.13   
16068406      5286.11         269.35       HBEFA3/Bus          2.25   
2078171       4237.56           4.17  HBEFA3/PC_G_EU4          1.82   
8433552          0.00          55.50  HBEFA3/PC_G_EU4          0.00   

          vehicle_noise  vehicle_pos    vehicle_route  vehicle_speed  \
7638310       -1.075224         2.67  !veh11122!var#1      -1.201680   
11969572      -1.411014        77.33  !veh16062!var#1      -1.507755   
16068406       0.101393        52.65      pt_bus_9B:0      -1.507755   
2078171        0.045879       227.06   !veh2109!var#1       0.251605   
8433552       -2.934252         7.53  !veh10684!var#1      -1.141599   

           vehicle_type  vehicle_waiting  
7638310   veh_passenger        -0.178627  
11969572  veh_passenger         0.336253  
16068406         pt_bus         1.520476  
2078171   veh_passenger        -0.178627  
8433552   veh_passenger        -0.178627


train_df


# Create an empty list
feature_cols = []


# Numeric Columns
for header in ["vehicle_fuel", "vehicle_speed", "vehicle_angle", "vehicle_noise"]: ### Finish the list on the left
    col = tf.feature_column.numeric_column(header)
    feature_cols.append(col)
    ### Insert your code ###

# Indicator Columns
indicator_col_names = ["vehicle_eclass", "vehicle_type"]
for col_name in indicator_col_names:
    categorical_column = tf.feature_column.categorical_column_with_vocabulary_list(col_name, 
                                                                                   train_df[col_name].unique())
    indicator_column = tf.feature_column.indicator_column(categorical_column)
    feature_cols.append(indicator_column)

print("Feature columns: ", feature_cols, "\n")

Feature columns:  [NumericColumn(key='vehicle_fuel', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='vehicle_speed', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='vehicle_angle', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='vehicle_noise', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='vehicle_eclass', vocabulary_list=('HBEFA3/PC_G_EU4', 'HBEFA3/Bus', 'HBEFA3/HDV', 'HBEFA3/LDV_G_EU6'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='vehicle_type', vocabulary_list=('veh_passenger', 'pt_bus', 'truck_truck', 'moto_motorcycle', 'bus_bus'), dtype=tf.string, default_value=-1, num_oov_buckets=0))]


# Create a feature layer for tf
feature_layer = tf.keras.layers.DenseFeatures(feature_cols, name='Features')


train_lbl = np.array(train_df["vehicle_CO2"])
train_df = train_df.drop(columns=["vehicle_CO2"])
# Split the datasets into features and label.
train_ft = {name:np.array(value) for name, value in train_df.items()}
# train_lbl = np.array(train_ft.pop(label_name))

val_lbl =  np.array(val_df["vehicle_CO2"])
val_df = val_df.drop(columns=["vehicle_CO2"])
val_ft = {name:np.array(value) for name, value in val_df.items()}

test_lbl = np.array(test_df["vehicle_CO2"])
test_df = test_df.drop(columns=["vehicle_CO2"])
test_ft = {key:np.array(value) for key, value in test_df.items()}


# Hyperparameters
learning_rate = 0.00025 
epochs = 100
batch_size = 200

# Label 
label_name = "vehicle_CO2"
shuffle = True

#---Create a sequential model---#
model = tf.keras.models.Sequential([
    # Add the feature layer
    feature_layer,

    # First hidden layer with 20 nodes
    tf.keras.layers.Dense(units=50, 
                          activation='relu',
                          kernel_regularizer=tf.keras.regularizers.l1(l=0.01),
                          name='Hidden1'),

    # Second hidden layer with 10 nodes
    tf.keras.layers.Dense(units=20, 
                          activation='relu',
                          kernel_regularizer=tf.keras.regularizers.l1(l=0.01),
                          name='Hidden2'),

    tf.keras.layers.Dense(units=10, 
                          activation='relu',
                          kernel_regularizer=tf.keras.regularizers.l1(l=0.01),
                          name='Hidden3'),

    # Output layer
    tf.keras.layers.Dense(units=1,  
                          activation='softplus',
                          name='Output')

])


model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
              loss=tf.keras.losses.MeanSquaredError(),
              metrics=['mse'])


#---Train the Model---#
# Keras TensorBoard callback.
logdir = "logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)



# Keras TensorBoard callback.
logdir = "logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)

model.fit(x=train_ft, y=train_lbl, batch_size=batch_size,
          epochs=epochs, callbacks=[tensorboard_callback], validation_data=(val_ft, val_lbl), shuffle=shuffle)

# Training function for large training set
# model.fit(x=train_ft, y=train_lbl, batch_size=batch_size,
#           epochs=epochs, verbose=2, validation_data=(val_ft, val_lbl), shuffle=shuffle)

2021-11-20 21:48:37.862127: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2021-11-20 21:48:37.862196: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2021-11-20 21:48:37.862222: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (DESKTOP-MCGAV6B): /proc/driver/nvidia/version does not exist
2021-11-20 21:48:37.862496: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/nick/.virtualenvs/iotlab4/lib/python3.8/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)

Epoch 1/100
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'vehicle_angle': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=float32>, 'vehicle_eclass': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'vehicle_fuel': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float32>, 'vehicle_noise': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=float32>, 'vehicle_pos': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=float32>, 'vehicle_route': <tf.Tensor 'IteratorGetNext:5' shape=(None,) dtype=string>, 'vehicle_speed': <tf.Tensor 'IteratorGetNext:6' shape=(None,) dtype=float32>, 'vehicle_type': <tf.Tensor 'IteratorGetNext:7' shape=(None,) dtype=string>, 'vehicle_waiting': <tf.Tensor 'IteratorGetNext:8' shape=(None,) dtype=float32>}
Consider rewriting this model with the Functional API.
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'vehicle_angle': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=float32>, 'vehicle_eclass': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'vehicle_fuel': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float32>, 'vehicle_noise': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=float32>, 'vehicle_pos': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=float32>, 'vehicle_route': <tf.Tensor 'IteratorGetNext:5' shape=(None,) dtype=string>, 'vehicle_speed': <tf.Tensor 'IteratorGetNext:6' shape=(None,) dtype=float32>, 'vehicle_type': <tf.Tensor 'IteratorGetNext:7' shape=(None,) dtype=string>, 'vehicle_waiting': <tf.Tensor 'IteratorGetNext:8' shape=(None,) dtype=float32>}
Consider rewriting this model with the Functional API.
656/662 [============================>.] - ETA: 0s - loss: 79330544.0000 - mse: 79330544.0000WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'vehicle_angle': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=float32>, 'vehicle_eclass': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'vehicle_fuel': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float32>, 'vehicle_noise': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=float32>, 'vehicle_pos': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=float32>, 'vehicle_route': <tf.Tensor 'IteratorGetNext:5' shape=(None,) dtype=string>, 'vehicle_speed': <tf.Tensor 'IteratorGetNext:6' shape=(None,) dtype=float32>, 'vehicle_type': <tf.Tensor 'IteratorGetNext:7' shape=(None,) dtype=string>, 'vehicle_waiting': <tf.Tensor 'IteratorGetNext:8' shape=(None,) dtype=float32>}
Consider rewriting this model with the Functional API.
662/662 [==============================] - 3s 4ms/step - loss: 79184344.0000 - mse: 79184344.0000 - val_loss: 70727368.0000 - val_mse: 70727368.0000
Epoch 2/100
662/662 [==============================] - 2s 3ms/step - loss: 65018128.0000 - mse: 65018128.0000 - val_loss: 63399000.0000 - val_mse: 63398996.0000
Epoch 3/100
662/662 [==============================] - 2s 3ms/step - loss: 49964048.0000 - mse: 49964044.0000 - val_loss: 35209080.0000 - val_mse: 35209076.0000
Epoch 4/100
662/662 [==============================] - 2s 3ms/step - loss: 16127032.0000 - mse: 16127020.0000 - val_loss: 5357274.5000 - val_mse: 5357267.0000
Epoch 5/100
662/662 [==============================] - 2s 3ms/step - loss: 3090708.2500 - mse: 3090700.0000 - val_loss: 2276020.7500 - val_mse: 2276012.7500
Epoch 6/100
662/662 [==============================] - 2s 3ms/step - loss: 1692573.6250 - mse: 1692566.1250 - val_loss: 1372377.0000 - val_mse: 1372368.7500
Epoch 7/100
662/662 [==============================] - 2s 3ms/step - loss: 1007589.5625 - mse: 1007581.9375 - val_loss: 809112.5000 - val_mse: 809104.5000
Epoch 8/100
662/662 [==============================] - 2s 3ms/step - loss: 560044.6250 - mse: 560036.4375 - val_loss: 424189.8750 - val_mse: 424182.3750
Epoch 9/100
662/662 [==============================] - 2s 3ms/step - loss: 279927.9375 - mse: 279920.8125 - val_loss: 194143.5469 - val_mse: 194136.0000
Epoch 10/100
662/662 [==============================] - 2s 4ms/step - loss: 120130.2734 - mse: 120122.6172 - val_loss: 81550.8281 - val_mse: 81543.2969
Epoch 11/100
662/662 [==============================] - 2s 4ms/step - loss: 52648.6094 - mse: 52641.1211 - val_loss: 39710.3633 - val_mse: 39702.8281
Epoch 12/100
662/662 [==============================] - 2s 4ms/step - loss: 28584.6523 - mse: 28577.1328 - val_loss: 24565.3086 - val_mse: 24557.7852
Epoch 13/100
662/662 [==============================] - 2s 4ms/step - loss: 18964.7520 - mse: 18957.2441 - val_loss: 16506.0078 - val_mse: 16498.4941
Epoch 14/100
662/662 [==============================] - 2s 4ms/step - loss: 13263.4209 - mse: 13255.9150 - val_loss: 11562.5117 - val_mse: 11555.0176
Epoch 15/100
662/662 [==============================] - 2s 4ms/step - loss: 9290.7842 - mse: 9283.2930 - val_loss: 8341.3701 - val_mse: 8333.8809
Epoch 16/100
662/662 [==============================] - 3s 4ms/step - loss: 6590.5698 - mse: 6583.0913 - val_loss: 5607.2427 - val_mse: 5599.7637
Epoch 17/100
662/662 [==============================] - 2s 4ms/step - loss: 4637.5386 - mse: 4630.0630 - val_loss: 3949.8479 - val_mse: 3942.3826
Epoch 18/100
662/662 [==============================] - 2s 4ms/step - loss: 3266.8867 - mse: 3259.4277 - val_loss: 2739.9700 - val_mse: 2732.5144
Epoch 19/100
662/662 [==============================] - 2s 4ms/step - loss: 2243.1565 - mse: 2235.7070 - val_loss: 1812.6089 - val_mse: 1805.1610
Epoch 20/100
662/662 [==============================] - 2s 4ms/step - loss: 1562.9683 - mse: 1555.5232 - val_loss: 1342.1046 - val_mse: 1334.6573
Epoch 21/100
662/662 [==============================] - 2s 4ms/step - loss: 1130.0829 - mse: 1122.6382 - val_loss: 896.1839 - val_mse: 888.7385
Epoch 22/100
662/662 [==============================] - 2s 4ms/step - loss: 849.8592 - mse: 842.4114 - val_loss: 754.9175 - val_mse: 747.4648
Epoch 23/100
662/662 [==============================] - 2s 4ms/step - loss: 743.2767 - mse: 735.8193 - val_loss: 596.7343 - val_mse: 589.2687
Epoch 24/100
662/662 [==============================] - 3s 4ms/step - loss: 597.7314 - mse: 590.2531 - val_loss: 474.6888 - val_mse: 467.1915
Epoch 25/100
662/662 [==============================] - 2s 3ms/step - loss: 480.4693 - mse: 472.9565 - val_loss: 379.8245 - val_mse: 372.2978
Epoch 26/100
662/662 [==============================] - 2s 4ms/step - loss: 385.4793 - mse: 377.9421 - val_loss: 405.6169 - val_mse: 398.0688
Epoch 27/100
662/662 [==============================] - 2s 4ms/step - loss: 349.0853 - mse: 341.5297 - val_loss: 372.6129 - val_mse: 365.0504
Epoch 28/100
662/662 [==============================] - 2s 4ms/step - loss: 315.6259 - mse: 308.0577 - val_loss: 335.0668 - val_mse: 327.4929
Epoch 29/100
662/662 [==============================] - 2s 3ms/step - loss: 296.7340 - mse: 289.1563 - val_loss: 226.8197 - val_mse: 219.2377
Epoch 30/100
662/662 [==============================] - 2s 4ms/step - loss: 267.8750 - mse: 260.2901 - val_loss: 235.0442 - val_mse: 227.4561
Epoch 31/100
662/662 [==============================] - 2s 4ms/step - loss: 252.7023 - mse: 245.1116 - val_loss: 276.1638 - val_mse: 268.5700
Epoch 32/100
662/662 [==============================] - 2s 4ms/step - loss: 228.7534 - mse: 221.1579 - val_loss: 184.3275 - val_mse: 176.7292
Epoch 33/100
662/662 [==============================] - 2s 4ms/step - loss: 217.7415 - mse: 210.1419 - val_loss: 183.8622 - val_mse: 176.2604
Epoch 34/100
662/662 [==============================] - 2s 4ms/step - loss: 213.4106 - mse: 205.8080 - val_loss: 176.6708 - val_mse: 169.0678
Epoch 35/100
662/662 [==============================] - 2s 4ms/step - loss: 195.8635 - mse: 188.2589 - val_loss: 261.1991 - val_mse: 253.5918
Epoch 36/100
662/662 [==============================] - 2s 3ms/step - loss: 186.6559 - mse: 179.0488 - val_loss: 188.1329 - val_mse: 180.5242
Epoch 37/100
662/662 [==============================] - 2s 4ms/step - loss: 168.9338 - mse: 161.3241 - val_loss: 151.6991 - val_mse: 144.0888
Epoch 38/100
662/662 [==============================] - 2s 3ms/step - loss: 166.4013 - mse: 158.7903 - val_loss: 126.9212 - val_mse: 119.3103
Epoch 39/100
662/662 [==============================] - 2s 3ms/step - loss: 162.2242 - mse: 154.6139 - val_loss: 173.9848 - val_mse: 166.3758
Epoch 40/100
662/662 [==============================] - 2s 3ms/step - loss: 155.0030 - mse: 147.3941 - val_loss: 369.8058 - val_mse: 362.1980
Epoch 41/100
662/662 [==============================] - 2s 3ms/step - loss: 143.8550 - mse: 136.2464 - val_loss: 238.0962 - val_mse: 230.4865
Epoch 42/100
662/662 [==============================] - 2s 3ms/step - loss: 140.4258 - mse: 132.8163 - val_loss: 103.0920 - val_mse: 95.4828
Epoch 43/100
662/662 [==============================] - 2s 3ms/step - loss: 126.0178 - mse: 118.4073 - val_loss: 106.7823 - val_mse: 99.1694
Epoch 44/100
662/662 [==============================] - 2s 3ms/step - loss: 120.4682 - mse: 112.8540 - val_loss: 176.0248 - val_mse: 168.4082
Epoch 45/100
662/662 [==============================] - 2s 3ms/step - loss: 131.9019 - mse: 124.2833 - val_loss: 194.4661 - val_mse: 186.8451
Epoch 46/100
662/662 [==============================] - 2s 3ms/step - loss: 112.4006 - mse: 104.7775 - val_loss: 88.1086 - val_mse: 80.4838
Epoch 47/100
662/662 [==============================] - 2s 3ms/step - loss: 108.4361 - mse: 100.8089 - val_loss: 156.1286 - val_mse: 148.4997
Epoch 48/100
662/662 [==============================] - 2s 3ms/step - loss: 129.2126 - mse: 121.5826 - val_loss: 160.3470 - val_mse: 152.7151
Epoch 49/100
662/662 [==============================] - 2s 3ms/step - loss: 96.5157 - mse: 88.8832 - val_loss: 83.4677 - val_mse: 75.8335
Epoch 50/100
662/662 [==============================] - 2s 3ms/step - loss: 104.0960 - mse: 96.4595 - val_loss: 115.8624 - val_mse: 108.2236
Epoch 51/100
662/662 [==============================] - 2s 3ms/step - loss: 103.9950 - mse: 96.3543 - val_loss: 96.5157 - val_mse: 88.8730
Epoch 52/100
662/662 [==============================] - 2s 3ms/step - loss: 110.2892 - mse: 102.6449 - val_loss: 79.4264 - val_mse: 71.7808
Epoch 53/100
662/662 [==============================] - 2s 3ms/step - loss: 96.4508 - mse: 88.8035 - val_loss: 100.0389 - val_mse: 92.3898
Epoch 54/100
662/662 [==============================] - 2s 3ms/step - loss: 93.1556 - mse: 85.5042 - val_loss: 402.6776 - val_mse: 395.0251
Epoch 55/100
662/662 [==============================] - 2s 3ms/step - loss: 109.6097 - mse: 101.9547 - val_loss: 157.7052 - val_mse: 150.0488
Epoch 56/100
662/662 [==============================] - 2s 3ms/step - loss: 96.8001 - mse: 89.1417 - val_loss: 95.8780 - val_mse: 88.2173
Epoch 57/100
662/662 [==============================] - 2s 3ms/step - loss: 93.0554 - mse: 85.3929 - val_loss: 76.2172 - val_mse: 68.5541
Epoch 58/100
662/662 [==============================] - 2s 3ms/step - loss: 91.4991 - mse: 83.8344 - val_loss: 71.3347 - val_mse: 63.6681
Epoch 59/100
662/662 [==============================] - 2s 3ms/step - loss: 109.8334 - mse: 102.1658 - val_loss: 130.9901 - val_mse: 123.3204
Epoch 60/100
662/662 [==============================] - 2s 3ms/step - loss: 87.7392 - mse: 80.0689 - val_loss: 69.0914 - val_mse: 61.4202
Epoch 61/100
662/662 [==============================] - 2s 3ms/step - loss: 86.0295 - mse: 78.3577 - val_loss: 137.1095 - val_mse: 129.4364
Epoch 62/100
662/662 [==============================] - 2s 3ms/step - loss: 91.6068 - mse: 83.9328 - val_loss: 68.5680 - val_mse: 60.8936
Epoch 63/100
662/662 [==============================] - 2s 3ms/step - loss: 86.4056 - mse: 78.7302 - val_loss: 65.8676 - val_mse: 58.1911
Epoch 64/100
662/662 [==============================] - 2s 3ms/step - loss: 91.6171 - mse: 83.9403 - val_loss: 67.8274 - val_mse: 60.1502
Epoch 65/100
662/662 [==============================] - 2s 3ms/step - loss: 88.6728 - mse: 80.9952 - val_loss: 77.6899 - val_mse: 70.0111
Epoch 66/100
662/662 [==============================] - 2s 3ms/step - loss: 83.9364 - mse: 76.2580 - val_loss: 72.8364 - val_mse: 65.1573
Epoch 67/100
662/662 [==============================] - 2s 3ms/step - loss: 90.8665 - mse: 83.1870 - val_loss: 76.1039 - val_mse: 68.4234
Epoch 68/100
662/662 [==============================] - 2s 3ms/step - loss: 86.8273 - mse: 79.1474 - val_loss: 112.5748 - val_mse: 104.8947
Epoch 69/100
662/662 [==============================] - 2s 3ms/step - loss: 89.4757 - mse: 81.7952 - val_loss: 63.8292 - val_mse: 56.1476
Epoch 70/100
662/662 [==============================] - 2s 3ms/step - loss: 83.6806 - mse: 75.9991 - val_loss: 150.1401 - val_mse: 142.4585
Epoch 71/100
662/662 [==============================] - 2s 3ms/step - loss: 83.8634 - mse: 76.1812 - val_loss: 64.5143 - val_mse: 56.8313
Epoch 72/100
662/662 [==============================] - 2s 3ms/step - loss: 86.4715 - mse: 78.7868 - val_loss: 107.5888 - val_mse: 99.9037
Epoch 73/100
662/662 [==============================] - 2s 3ms/step - loss: 84.0807 - mse: 76.3943 - val_loss: 83.1718 - val_mse: 75.4857
Epoch 74/100
662/662 [==============================] - 2s 3ms/step - loss: 82.3913 - mse: 74.7021 - val_loss: 176.5017 - val_mse: 168.8111
Epoch 75/100
662/662 [==============================] - 2s 3ms/step - loss: 86.8000 - mse: 79.1083 - val_loss: 72.1226 - val_mse: 64.4287
Epoch 76/100
662/662 [==============================] - 2s 3ms/step - loss: 86.5047 - mse: 78.8102 - val_loss: 63.9386 - val_mse: 56.2433
Epoch 77/100
662/662 [==============================] - 2s 3ms/step - loss: 81.0029 - mse: 73.3064 - val_loss: 67.7209 - val_mse: 60.0233
Epoch 78/100
662/662 [==============================] - 2s 3ms/step - loss: 80.0911 - mse: 72.3917 - val_loss: 59.3782 - val_mse: 51.6779
Epoch 79/100
662/662 [==============================] - 2s 3ms/step - loss: 83.0124 - mse: 75.3110 - val_loss: 232.4343 - val_mse: 224.7319
Epoch 80/100
662/662 [==============================] - 2s 3ms/step - loss: 78.2458 - mse: 70.5423 - val_loss: 65.7999 - val_mse: 58.0953
Epoch 81/100
662/662 [==============================] - 2s 3ms/step - loss: 85.9389 - mse: 78.2333 - val_loss: 66.6091 - val_mse: 58.9020
Epoch 82/100
662/662 [==============================] - 2s 3ms/step - loss: 76.0563 - mse: 68.3487 - val_loss: 69.1832 - val_mse: 61.4747
Epoch 83/100
662/662 [==============================] - 2s 3ms/step - loss: 85.4043 - mse: 77.6949 - val_loss: 63.2030 - val_mse: 55.4930
Epoch 84/100
662/662 [==============================] - 2s 3ms/step - loss: 73.5612 - mse: 65.8489 - val_loss: 69.1200 - val_mse: 61.4062
Epoch 85/100
662/662 [==============================] - 2s 3ms/step - loss: 83.0800 - mse: 75.3660 - val_loss: 58.9890 - val_mse: 51.2741
Epoch 86/100
662/662 [==============================] - 2s 3ms/step - loss: 72.7144 - mse: 64.9987 - val_loss: 60.7145 - val_mse: 52.9978
Epoch 87/100
662/662 [==============================] - 2s 3ms/step - loss: 78.4185 - mse: 70.7007 - val_loss: 56.1925 - val_mse: 48.4729
Epoch 88/100
662/662 [==============================] - 2s 3ms/step - loss: 79.3547 - mse: 71.6343 - val_loss: 65.6896 - val_mse: 57.9678
Epoch 89/100
662/662 [==============================] - 2s 3ms/step - loss: 76.8642 - mse: 69.1408 - val_loss: 95.4288 - val_mse: 87.7053
Epoch 90/100
662/662 [==============================] - 2s 3ms/step - loss: 74.1398 - mse: 66.4144 - val_loss: 55.6624 - val_mse: 47.9354
Epoch 91/100
662/662 [==============================] - 2s 3ms/step - loss: 79.8749 - mse: 72.1469 - val_loss: 61.7292 - val_mse: 54.0004
Epoch 92/100
662/662 [==============================] - 2s 3ms/step - loss: 78.9912 - mse: 71.2609 - val_loss: 53.7035 - val_mse: 45.9720
Epoch 93/100
662/662 [==============================] - 2s 3ms/step - loss: 75.8728 - mse: 68.1391 - val_loss: 57.1908 - val_mse: 49.4551
Epoch 94/100
662/662 [==============================] - 2s 3ms/step - loss: 74.8501 - mse: 67.1128 - val_loss: 72.1238 - val_mse: 64.3837
Epoch 95/100
662/662 [==============================] - 2s 3ms/step - loss: 75.5248 - mse: 67.7846 - val_loss: 263.8614 - val_mse: 256.1207
Epoch 96/100
662/662 [==============================] - 2s 3ms/step - loss: 82.8836 - mse: 75.1415 - val_loss: 85.4384 - val_mse: 77.6948
Epoch 97/100
662/662 [==============================] - 2s 3ms/step - loss: 73.7890 - mse: 66.0442 - val_loss: 98.3216 - val_mse: 90.5759
Epoch 98/100
662/662 [==============================] - 2s 3ms/step - loss: 77.0843 - mse: 69.3374 - val_loss: 52.3031 - val_mse: 44.5554
Epoch 99/100
662/662 [==============================] - 2s 3ms/step - loss: 77.2531 - mse: 69.5041 - val_loss: 58.5692 - val_mse: 50.8194
Epoch 100/100
662/662 [==============================] - 2s 3ms/step - loss: 73.7785 - mse: 66.0277 - val_loss: 55.7968 - val_mse: 48.0450

<keras.callbacks.History at 0x7f9d279baeb0>


# test_lbl = np.array(test_ft.pop(label_name))
print("Model evaluation: \n")
model.evaluate(x=test_ft, y=test_lbl, batch_size=batch_size)

Model evaluation: 

82/82 [==============================] - 0s 2ms/step - loss: 56.4856 - mse: 48.7338

[56.48563766479492, 48.733795166015625]


#Get a summary of your model
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 Features (DenseFeatures)    multiple                  0         
                                                                 
 Hidden1 (Dense)             multiple                  700       
                                                                 
 Hidden2 (Dense)             multiple                  1020      
                                                                 
 Hidden3 (Dense)             multiple                  210       
                                                                 
 Output (Dense)              multiple                  11        
                                                                 
=================================================================
Total params: 1,941
Trainable params: 1,941
Non-trainable params: 0
_________________________________________________________________


%tensorboard --logdir logs


%%time
# Get the features from the test set
test_features = test_ft
# Get the actual CO2 output for the test set 
actual_labels = test_lbl

# Make prediction on the test set
predicted_labels = model.predict(x=test_features).flatten()

# Define the graph
Figure1 = plt.figure(figsize=(5,5), dpi=100)
plt.xlabel('Actual Outputs [Vehicle CO\u2082]')
plt.ylabel('Predicted Outputs [Vehicle CO\u2082]')
plt.scatter(actual_labels, predicted_labels, s=15, c='Red', edgecolors='Yellow', label='Predicted Values')

# Take the output data from 2000 to 3000 as an instance to visualize 
lims = [2000, 3000]
plt.xlim(lims)
plt.ylim(lims)
plt.plot(lims, lims, color='Green', label='Targeted Values')
plt.legend()

WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'vehicle_angle': <tf.Tensor 'IteratorGetNext:0' shape=(None,) dtype=float32>, 'vehicle_eclass': <tf.Tensor 'IteratorGetNext:1' shape=(None,) dtype=string>, 'vehicle_fuel': <tf.Tensor 'IteratorGetNext:2' shape=(None,) dtype=float32>, 'vehicle_noise': <tf.Tensor 'IteratorGetNext:3' shape=(None,) dtype=float32>, 'vehicle_pos': <tf.Tensor 'IteratorGetNext:4' shape=(None,) dtype=float32>, 'vehicle_route': <tf.Tensor 'IteratorGetNext:5' shape=(None,) dtype=string>, 'vehicle_speed': <tf.Tensor 'IteratorGetNext:6' shape=(None,) dtype=float32>, 'vehicle_type': <tf.Tensor 'IteratorGetNext:7' shape=(None,) dtype=string>, 'vehicle_waiting': <tf.Tensor 'IteratorGetNext:8' shape=(None,) dtype=float32>}
Consider rewriting this model with the Functional API.
CPU times: user 1.7 s, sys: 517 ms, total: 2.22 s
Wall time: 907 ms

<matplotlib.legend.Legend at 0x7f9d2b6e6580>


error = actual_labels - predicted_labels
Figure2 = plt.figure(figsize=(8,3), dpi=100)
plt.hist(error, bins=50, color='Red', edgecolor='Green')
plt.xlabel('Prediction Error [Vehicle CO\u2082]')
plt.ylabel('Count')

Text(0, 0.5, 'Count')


from IPython.display import HTML, display

def display_table(data_x, data_y):
    html = "<table>"
    html += "<tr>"
    html += "<td><h3>%s</h3><td>"%"Actual Vehicle CO\u2082"
    html += "<td><h3>%s</h3><td>"%"Predicted Vehicle CO\u2082"
    html += "</tr>"
    for i in range(len(data_x)):
        html += "<tr>"
        html += "<td><h4>%s</h4><td>"%(int(data_x[i]))
        html += "<td><h4>%s</h4><td>"%(int(data_y[i]))
        html += "</tr>"
    html += "</table>"
    display(HTML(html))
    
display_table(actual_labels[0:100], predicted_labels[0:100])

	timestep_time	vehicle_CO	vehicle_CO2	vehicle_HC	vehicle_NOx	vehicle_PMx	vehicle_angle	vehicle_eclass	vehicle_electricity	vehicle_fuel	vehicle_id	vehicle_lane	vehicle_noise	vehicle_pos	vehicle_route	vehicle_speed	vehicle_type	vehicle_waiting	vehicle_x	vehicle_y
0	0.0	15.20	7380.56	0.00	84.89	2.21	50.28	HBEFA3/HDV	0.0	3.13	truck0	5329992#5_0	67.11	7.20	!truck0!var#1	0.00	truck_truck	0.0	18275.04	26987.78
1	0.0	0.00	2416.04	0.01	0.72	0.01	42.25	HBEFA3/PC_G_EU4	0.0	1.04	veh0	5330181#0_0	65.15	5.10	!veh0!var#1	14.72	veh_passenger	0.0	18279.94	24533.12
2	1.0	17.92	9898.93	0.00	103.38	2.49	50.28	HBEFA3/HDV	0.0	4.20	truck0	5329992#5_0	73.20	8.21	!truck0!var#1	1.01	truck_truck	0.0	18275.82	26988.43
3	1.0	0.00	0.00	0.00	0.00	0.00	42.25	HBEFA3/PC_G_EU4	0.0	0.00	veh0	5330181#0_0	62.72	18.85	!veh0!var#1	13.75	veh_passenger	0.0	18289.19	24543.30
4	1.0	164.78	2624.72	0.81	1.20	0.07	357.00	HBEFA3/PC_G_EU4	0.0	1.13	veh1	-5338968#2_0	55.94	5.10	!veh1!var#1	0.00	veh_passenger	0.0	29252.01	24424.16
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
95	7.0	23.44	2578.06	0.15	0.64	0.05	0.13	HBEFA3/LDV_G_EU6	0.0	1.11	moto2	-5341858#10_0	63.24	35.78	!moto2!var#1	11.62	moto_motorcycle	0.0	26468.26	25548.47
96	7.0	732.32	18759.70	3.34	3.79	1.19	179.93	HBEFA3/LDV_G_EU6	0.0	8.07	moto3	-342586098#36_0	81.67	30.96	!moto3!var#1	13.99	moto_motorcycle	0.0	24729.15	27450.68
97	7.0	294.68	6949.38	1.29	1.47	0.43	179.93	HBEFA3/LDV_G_EU6	0.0	2.99	moto4	5331636#0_0	72.45	11.88	!moto4!var#1	6.37	moto_motorcycle	0.0	29159.96	25066.29
98	7.0	236.07	4292.19	0.97	0.93	0.30	1.91	HBEFA3/LDV_G_EU6	0.0	1.85	moto5	5340657#0_0	71.73	5.60	!moto5!var#1	3.30	moto_motorcycle	0.0	24340.58	28198.87
99	7.0	179.19	1228.61	0.64	0.31	0.17	180.06	HBEFA3/LDV_G_EU6	0.0	0.53	moto6	5339596#0_0	55.94	2.30	!moto6!var#1	0.00	moto_motorcycle	0.0	26577.70	25847.92

	timestep_time	vehicle_CO	vehicle_CO2	vehicle_HC	vehicle_NOx	vehicle_PMx	vehicle_angle	vehicle_electricity	vehicle_fuel	vehicle_noise	vehicle_pos	vehicle_speed	vehicle_waiting	vehicle_x	vehicle_y
count	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	16331007.0	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07
mean	4.112561e+03	5.764304e+01	4.919050e+03	7.284125e-01	1.769589e+01	4.227491e-01	1.633698e+02	0.0	2.105266e+00	6.636207e+01	2.162082e+02	1.331140e+01	3.385107e+00	2.458506e+04	2.496505e+04
std	2.168986e+03	8.854365e+01	7.959043e+03	1.589816e+00	5.993168e+01	1.164065e+00	1.051232e+02	0.0	3.389028e+00	7.389330e+00	6.034189e+02	8.833069e+00	1.914152e+01	4.016049e+03	3.045771e+03
min	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.0	0.000000e+00	1.258000e+01	0.000000e+00	0.000000e+00	0.000000e+00	9.960000e+00	-1.490000e+00
25%	2.291000e+03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	9.031000e+01	0.0	0.000000e+00	6.249000e+01	2.383000e+01	6.550000e+00	0.000000e+00	2.219207e+04	2.349907e+04
50%	4.133000e+03	2.017000e+01	2.624720e+03	1.500000e-01	1.200000e+00	6.000000e-02	1.799600e+02	0.0	1.130000e+00	6.711000e+01	7.199000e+01	1.337000e+01	0.000000e+00	2.393805e+04	2.548033e+04
75%	5.903000e+03	1.034400e+02	6.161010e+03	7.600000e-01	2.710000e+00	1.500000e-01	2.703500e+02	0.0	2.650000e+00	7.112000e+01	1.780600e+02	1.999000e+01	0.000000e+00	2.691704e+04	2.672322e+04
max	1.441800e+04	3.932950e+03	1.153026e+05	1.729000e+01	8.864200e+02	1.432000e+01	3.600000e+02	0.0	4.888000e+01	1.019600e+02	1.943554e+04	5.013000e+01	3.970000e+02	4.492832e+04	4.753314e+04

	vehicle_CO2	vehicle_angle	vehicle_eclass	vehicle_fuel	vehicle_noise	vehicle_pos	vehicle_route	vehicle_speed	vehicle_type	vehicle_waiting
0	7380.56	50.28	HBEFA3/HDV	3.13	67.11	7.20	!truck0!var#1	0.00	truck_truck	0.0
1	2416.04	42.25	HBEFA3/PC_G_EU4	1.04	65.15	5.10	!veh0!var#1	14.72	veh_passenger	0.0
2	9898.93	50.28	HBEFA3/HDV	4.20	73.20	8.21	!truck0!var#1	1.01	truck_truck	0.0
3	0.00	42.25	HBEFA3/PC_G_EU4	0.00	62.72	18.85	!veh0!var#1	13.75	veh_passenger	0.0
4	2624.72	357.00	HBEFA3/PC_G_EU4	1.13	55.94	5.10	!veh1!var#1	0.00	veh_passenger	0.0
...	...	...	...	...	...	...	...	...	...	...
95	2578.06	0.13	HBEFA3/LDV_G_EU6	1.11	63.24	35.78	!moto2!var#1	11.62	moto_motorcycle	0.0
96	18759.70	179.93	HBEFA3/LDV_G_EU6	8.07	81.67	30.96	!moto3!var#1	13.99	moto_motorcycle	0.0
97	6949.38	179.93	HBEFA3/LDV_G_EU6	2.99	72.45	11.88	!moto4!var#1	6.37	moto_motorcycle	0.0
98	4292.19	1.91	HBEFA3/LDV_G_EU6	1.85	71.73	5.60	!moto5!var#1	3.30	moto_motorcycle	0.0
99	1228.61	180.06	HBEFA3/LDV_G_EU6	0.53	55.94	2.30	!moto6!var#1	0.00	moto_motorcycle	0.0

	vehicle_CO2	vehicle_angle	vehicle_fuel	vehicle_noise	vehicle_pos	vehicle_speed	vehicle_waiting
count	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07	1.633101e+07
mean	4.919050e+03	1.633698e+02	2.105266e+00	6.636207e+01	2.162082e+02	1.331140e+01	3.385107e+00
std	7.959043e+03	1.051232e+02	3.389028e+00	7.389330e+00	6.034189e+02	8.833069e+00	1.914152e+01
min	0.000000e+00	0.000000e+00	0.000000e+00	1.258000e+01	0.000000e+00	0.000000e+00	0.000000e+00
25%	0.000000e+00	9.031000e+01	0.000000e+00	6.249000e+01	2.383000e+01	6.550000e+00	0.000000e+00
50%	2.624720e+03	1.799600e+02	1.130000e+00	6.711000e+01	7.199000e+01	1.337000e+01	0.000000e+00
75%	6.161010e+03	2.703500e+02	2.650000e+00	7.112000e+01	1.780600e+02	1.999000e+01	0.000000e+00
max	1.153026e+05	3.600000e+02	4.888000e+01	1.019600e+02	1.943554e+04	5.013000e+01	3.970000e+02

	vehicle_CO2	vehicle_angle	vehicle_eclass	vehicle_fuel	vehicle_noise	vehicle_pos	vehicle_route	vehicle_speed	vehicle_type	vehicle_waiting
16330908	5293.91	1.98	HBEFA3/Bus	2.26	67.19	77.83	pt_bus_5E:0	0.01	pt_bus	1.0
16330909	6541.73	2.07	HBEFA3/Bus	2.79	71.21	0.69	pt_bus_5E:0	0.70	pt_bus	0.0
16330910	10387.44	2.06	HBEFA3/Bus	4.43	74.53	2.58	pt_bus_5E:0	1.88	pt_bus	0.0
16330911	12058.39	1.62	HBEFA3/Bus	5.14	73.88	5.45	pt_bus_5E:0	2.87	pt_bus	0.0
16330912	13307.66	1.06	HBEFA3/Bus	5.67	73.64	9.19	pt_bus_5E:0	3.74	pt_bus	0.0
...	...	...	...	...	...	...	...	...	...	...
16331003	19817.16	0.45	HBEFA3/Bus	8.45	76.56	185.84	pt_bus_5E:0	13.65	pt_bus	0.0
16331004	0.00	0.45	HBEFA3/Bus	0.00	74.14	199.17	pt_bus_5E:0	13.33	pt_bus	0.0
16331005	23192.37	0.45	HBEFA3/Bus	9.89	77.18	212.90	pt_bus_5E:0	13.73	pt_bus	0.0
16331006	0.00	0.45	HBEFA3/Bus	0.00	74.10	226.29	pt_bus_5E:0	13.39	pt_bus	0.0
16331007	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	vehicle_CO2	vehicle_angle	vehicle_eclass	vehicle_fuel	vehicle_noise	vehicle_pos	vehicle_route	vehicle_speed	vehicle_type	vehicle_waiting
2834601	0.00	180.43	HBEFA3/PC_G_EU4	0.00	47.71	72.82	!veh4685!var#1	4.46	veh_passenger	0.0
12665489	5574.18	270.37	HBEFA3/PC_G_EU4	2.40	66.53	18.30	!veh15679!var#1	8.84	veh_passenger	0.0
3755595	26332.65	180.32	HBEFA3/HDV	11.16	78.19	12.45	!truck352!var#1	15.59	truck_truck	0.0
1695537	6544.37	90.42	HBEFA3/PC_G_EU4	2.81	68.26	42.76	!veh3276!var#1	15.21	veh_passenger	0.0
3896343	9367.20	0.12	HBEFA3/PC_G_EU4	4.03	72.51	141.08	!veh4654!var#1	23.68	veh_passenger	0.0
...	...	...	...	...	...	...	...	...	...	...
1472395	0.00	180.19	HBEFA3/PC_G_EU4	0.00	55.04	116.59	!veh2374!var#1	6.11	veh_passenger	0.0
3685158	0.00	271.47	HBEFA3/PC_G_EU4	0.00	61.63	53.35	!veh5458!var#1	13.93	veh_passenger	0.0
14020916	0.00	179.93	HBEFA3/PC_G_EU4	0.00	55.94	92.25	!veh17020!var#1	0.00	veh_passenger	4.0
4428417	0.00	270.24	HBEFA3/PC_G_EU4	0.00	72.54	760.05	!veh5819!var#1	27.84	veh_passenger	0.0
4388932	0.00	18.44	HBEFA3/PC_G_EU4	0.00	66.96	9.30	!veh5855!var#1	18.56	veh_passenger	0.0

	vehicle_CO2	vehicle_angle	vehicle_eclass	vehicle_fuel	vehicle_noise	vehicle_pos	vehicle_route	vehicle_speed	vehicle_type	vehicle_waiting
7638310	2549.70	203.05	HBEFA3/PC_G_EU4	1.10	-1.075224	2.67	!veh11122!var#1	-1.201680	veh_passenger	-0.178627
11969572	2624.72	181.45	HBEFA3/PC_G_EU4	1.13	-1.411014	77.33	!veh16062!var#1	-1.507755	veh_passenger	0.336253
16068406	5286.11	269.35	HBEFA3/Bus	2.25	0.101393	52.65	pt_bus_9B:0	-1.507755	pt_bus	1.520476
2078171	4237.56	4.17	HBEFA3/PC_G_EU4	1.82	0.045879	227.06	!veh2109!var#1	0.251605	veh_passenger	-0.178627
8433552	0.00	55.50	HBEFA3/PC_G_EU4	0.00	-2.934252	7.53	!veh10684!var#1	-1.141599	veh_passenger	-0.178627
...	...	...	...	...	...	...	...	...	...	...
140030	4581.15	181.14	HBEFA3/PC_G_EU4	1.97	-0.187007	7.49	!veh724!var#1	-0.718763	veh_passenger	-0.178627
2048360	0.00	1.27	HBEFA3/PC_G_EU4	0.00	-1.412368	280.25	!veh2561!var#1	-1.507755	veh_passenger	-0.024163
7157891	0.00	90.80	HBEFA3/PC_G_EU4	0.00	-0.345424	181.85	!veh10553!var#1	-0.055603	veh_passenger	-0.178627
13558792	3474.49	94.34	HBEFA3/PC_G_EU4	1.49	-0.092228	7.14	!veh18582!var#1	-1.276499	veh_passenger	-0.178627
8306460	3394.11	271.91	HBEFA3/PC_G_EU4	1.46	0.311261	22.24	!veh10766!var#1	0.765130	veh_passenger	-0.178627

Actual Vehicle CO₂		Predicted Vehicle CO₂
5286		5284
0		0
2624		2625
8381		8373
9026		9019
16025		16039
0		0
0		0
3392		3398
0		0
5286		5287
2141		2146
0		0
6008		6003
0		0
7427		7421
2624		2624
7783		7785
0		0
0		0
4700		4702
0		0
0		0
0		0
0		0
0		0
0		0
3223		3237
5286		5282
10385		10355
0		0
7380		7390
13882		13891
45482		45460
5186		5189
0		0
0		0
2624		2627
18365		18371
5977		5971
0		0
16343		16365
0		0
0		0
2706		2700
0		0
6476		6463
9410		9408
7434		7445
0		0
6176		6164
7555		7562
0		0
18916		18917
0		0
0		0
7836		7840
0		0
0		0
0		0
2931		2933
3271		3283
0		0
5514		5522
0		0
0		0
3565		3560
12180		12192
3632		3631
0		0
8769		8772
0		0
5286		5290
0		0
10815		10819
7090		7091
0		0
0		0
0		0
29725		29729
0		0
0		0
3580		3582
6814		6816
2878		2885
6564		6562
5197		5192
5200		5213
0		0
5011		5000
0		0
0		0
0		0
0		0
3101		3097
2607		2605
7759		7772
4757		4771
0		0
0		0

Machine Learning on Polutions from Transportation¶

Import Libraries¶

Import Tensorboard¶

Load and Clean up the Dataset¶

Load the Dataset¶

Visualiz the Dataset¶

Clean up the Dataset¶

Stop¶

Split Data for Machine Learning¶

Normalize the Input Data (Optional)¶

Organize Features¶

Classify Features¶

Create a Feature Layer¶

Create and Train the Model¶

Create Model¶

Train the Model¶

Instructions for Training Small and Large Data¶

Evaluate the Model with Test Data¶

Use the Trained-Model and Visualize the results¶

TensorBoard¶

Predict $CO_{2}$ From Trained-Model¶

Error Count Histogram¶

Table of Actual and Predicted Values¶

Actual Vehicle CO₂

Predicted Vehicle CO₂

5286

5284

0

0

2624

2625

8381

8373

9026

9019

16025

16039

0

0

0

0

3392

3398

0

0

5286

5287

2141

2146

0

0

6008

6003

0

0

7427

7421

2624

2624

7783

7785

0

0

0

0

4700

4702

0

0

0

0

0

0

0

0

0

0

0

0

3223